The Experts below are selected from a list of 45504 Experts worldwide ranked by ideXlab platform
Haibo Liu - One of the best experts on this subject based on the ideXlab platform.
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a nomogram prognostic model for large cell lung cancer analysis from the surveillance epidemiology and end results database
Translational lung cancer research, 2021Co-Authors: Gang Lin, Bing Liu, Haibo LiuAbstract:Background Currently, there is no reliable method for predicting the prognosis of patients with large cell lung cancer (LCLC). The aim of this study was to develop and validate a nomogram model for accurately predicting the prognosis of patients with LCLC. Methods LCLC patients, diagnosed from 2007 to 2009, were identified from the Surveillance, Epidemiology and End Results (SEER) database and used as the training Dataset. Significant clinicopathologic variables (P<0.05) in a multivariate Cox regression were selected to build the nomogram. The performance of the nomogram model was evaluated by the concordance index (C-index), the area under the curve (AUC), and internal calibration. LCLC patients diagnosed from 2010 to 2016 in the SEER database were selected as a Testing Dataset for external validation. The nomogram model was also compared with the currently used American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (8th edition) by using C-index and a decision curve analysis. Results Eight variables-age, sex, race, marital status, T stage, N stage, M stage, and treatment strategy-were statistically significant in the multivariate Cox model and were selected to develop the nomogram model. This model exhibited excellent predictive performance. The C-index and AUC value were 0.761 [95% confidence interval (CI), 0.754 to 0.768] and 0.886 for the training Dataset and 0.773 (95% CI, 0.765 to 0.781) and 0.876 for the Testing Dataset, respectively. This model also predicted three-year and five-year lung cancer-specific survival (LCSS) in both Datasets with good fidelity. This nomogram model performs significantly better than the 8th edition AJCC TNM staging system, with a higher C-index (P<0.001) and better net benefits in predicting LCSS in LCLC patients. Conclusions We developed and validated a prognostic nomogram model for predicting 3- and 5-year LCSS in LCLC patients with good discrimination and calibration abilities. The nomogram may be useful in assisting clinicians to make individualized decisions for appropriate treatment in LCLC.
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Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients
'Frontiers Media SA', 2021Co-Authors: Cangang Zhang, Haibo Liu, Zhe Zhao, Shukun Yao, Dongyan ZhaoAbstract:Colon adenocarcinoma (COAD) is one of the most common malignant tumors and has high migration and invasion capacity. In this study, we attempted to establish a multigene signature for predicting the prognosis of COAD patients. Weighted gene co-expression network analysis and differential gene expression analysis methods were first applied to identify differentially co-expressed genes between COAD tissues and normal tissues from the Cancer Genome Atlas (TCGA)-COAD Dataset and GSE39582 Dataset, and a total of 309 overlapping genes were screened out. Then, our study employed TCGA-COAD cohort as the training Dataset and an independent cohort by merging the GES39582 and GSE17536 Datasets as the Testing Dataset. After univariate and multivariate Cox regression analyses were performed for these overlapping genes and overall survival (OS) of COAD patients in the training Dataset, a 13-gene signature was constructed to divide COAD patients into high- and low-risk subgroups with significantly different OS. The Testing Dataset exhibited the same results utilizing the same predictive signature. The area under the curve of receiver operating characteristic analysis for predicting OS in the training and Testing Datasets were 0.789 and 0.868, respectively, which revealed the enhanced predictive power of the signature. Multivariate Cox regression analysis further suggested that the 13-gene signature could independently predict OS. Among the 13 prognostic genes, NAT1 and NAT2 were downregulated with deep deletions in tumor tissues in multiple COAD cohorts and exhibited significant correlations with poorer OS based on the GEPIA database. Notably, NAT1 and NAT2 expression levels were positively correlated with infiltrating levels of CD8+ T cells and dendritic cells, exhibiting a foundation for further research investigating the antitumor immune roles played by NAT1 and NAT2 in COAD. Taken together, the results of our study showed that the 13-gene signature could efficiently predict OS and that NAT1 and NAT2 could function as biomarkers for prognosis and the immune response in COAD
Vince D. Calhoun - One of the best experts on this subject based on the ideXlab platform.
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classification of schizophrenia patients based on resting state functional network connectivity
Frontiers in Neuroscience, 2013Co-Authors: Kent A. Kiehl, Godfrey D. Pearlson, Vince D. Calhoun, Mohammad R ArbabshiraniAbstract:There is a growing interest in automatic classification of mental disorders based on neuroimaging data. Small training data sets (subjects) and very large amount of high dimensional data make it a challenging task to design robust and accurate classifiers for heterogeneous disorders such as schizophrenia. Most previous studies considered structural MRI, diffusion tensor imaging and task-based fMRI for this purpose. However, resting-state data has been rarely used in discrimination of schizophrenia patients from healthy controls. Resting data are of great interest, since they are relatively easy to collect, and not confounded by behavioral performance on a task. Several linear and non-linear classification methods were trained using a training Dataset and evaluate with a separate Testing Dataset. Results show that classification with high accuracy is achievable using simple non-linear discriminative methods such as k-nearest neighbors which is very promising. We compare and report detailed results of each classifier as well as statistical analysis and evaluation of each single feature. To our knowledge our effects represent the first use of resting-state functional network connectivity features to classify schizophrenia.
Gang Lin - One of the best experts on this subject based on the ideXlab platform.
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a nomogram prognostic model for large cell lung cancer analysis from the surveillance epidemiology and end results database
Translational lung cancer research, 2021Co-Authors: Gang Lin, Bing Liu, Haibo LiuAbstract:Background Currently, there is no reliable method for predicting the prognosis of patients with large cell lung cancer (LCLC). The aim of this study was to develop and validate a nomogram model for accurately predicting the prognosis of patients with LCLC. Methods LCLC patients, diagnosed from 2007 to 2009, were identified from the Surveillance, Epidemiology and End Results (SEER) database and used as the training Dataset. Significant clinicopathologic variables (P<0.05) in a multivariate Cox regression were selected to build the nomogram. The performance of the nomogram model was evaluated by the concordance index (C-index), the area under the curve (AUC), and internal calibration. LCLC patients diagnosed from 2010 to 2016 in the SEER database were selected as a Testing Dataset for external validation. The nomogram model was also compared with the currently used American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (8th edition) by using C-index and a decision curve analysis. Results Eight variables-age, sex, race, marital status, T stage, N stage, M stage, and treatment strategy-were statistically significant in the multivariate Cox model and were selected to develop the nomogram model. This model exhibited excellent predictive performance. The C-index and AUC value were 0.761 [95% confidence interval (CI), 0.754 to 0.768] and 0.886 for the training Dataset and 0.773 (95% CI, 0.765 to 0.781) and 0.876 for the Testing Dataset, respectively. This model also predicted three-year and five-year lung cancer-specific survival (LCSS) in both Datasets with good fidelity. This nomogram model performs significantly better than the 8th edition AJCC TNM staging system, with a higher C-index (P<0.001) and better net benefits in predicting LCSS in LCLC patients. Conclusions We developed and validated a prognostic nomogram model for predicting 3- and 5-year LCSS in LCLC patients with good discrimination and calibration abilities. The nomogram may be useful in assisting clinicians to make individualized decisions for appropriate treatment in LCLC.
Xuannam Bui - One of the best experts on this subject based on the ideXlab platform.
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predicting blast induced air overpressure a robust artificial intelligence system based on artificial neural networks and random forest
Natural resources research, 2019Co-Authors: Hoang Nguyen, Xuannam BuiAbstract:Blasting is the most popular method for rock fragmentation in open-pit mines. However, the side effects caused by blasting operations include ground vibration, air overpressure (AOp), fly rock, back-break, dust, and toxic are the critical factors which have a significant impact on the surrounding environment, especially AOp. In this paper, a robust artificial intelligence system was developed for predicting blast-induced AOp based on artificial neural networks (ANNs) and random forest (RF), code name ANNs-RF. Five ANN models were developed first; then, the RF algorithm was used to combine them. An empirical technique, ANN, and RF models were also developed to predict and compare with the ANNs-RF model. For this aim, 114 blasting events were recorded at the Nui Beo open-pit coal mine, Vietnam. The maximum explosive charge capacity, monitoring distance, vertical distance, powder factor, burden, spacing, and length of stemming were used as the input variables for predicting AOp. The quality of the models is evaluated by root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and a simple ranking method. The results indicated that the proposed ANNs-RF model was the most superior model with RMSE of 0.847, R2 of 0.985, MAE of 0.620 on Testing Dataset, and total ranking of 40. In contrast, the best ANN model yielded a slightly lower performance with RMSE of 1.184, R2 of 0.960, MAE of 0.809, and a total ranking of 39; the RF model yielded a performance with RMSE of 1.550, R2 of 0.939, MAE of 1.222, and total ranking of 22; the empirical model provided the lowest accuracy level with RMSE of 5.704, R2 of 0.429, MAE of 5.316 on the Testing Dataset, and total ranking of 6.
Gregory V Goldmacher - One of the best experts on this subject based on the ideXlab platform.
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a deep learning facilitated radiomics solution for the prediction of lung lesion shrinkage in non small cell lung cancer trials
International Symposium on Biomedical Imaging, 2020Co-Authors: Antong Chen, Jennifer Saouaf, Bo Zhou, Randolph Crawford, Jianda Yuan, Richard Baumgartner, Shubing Wang, Gregory V GoldmacherAbstract:Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials. The approach starts with the classification of lung lesions from the set of primary and metastatic lesions at various anatomic locations. Focusing on the lung lesions, we perform automatic segmentation to extract their 3D volumes. Radiomic features are then extracted from the lesion on the pre-treatment scan and the first follow-up scan to predict which lesions will shrink at least 30% in diameter during treatment (either Pembrolizumab or combinations of chemotherapy and Pembrolizumab), which is defined as a partial response by the Response Evaluation Criteria In Solid Tumors (RECIST) guidelines. A 5-fold cross validation on the training set led to an AUC of 0.84 ± 0.03, and the prediction on the Testing Dataset reached AUC of 0.73 ± 0.02 for the outcome of 30% diameter shrinkage.
